{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": 61
   },
   "source": [
    "# RFM模型分析\n",
    " RFM的含义：\n",
    "​\n",
    "- R（Recency）：客户最近一次交易时间的间隔。R值越大，表示客户交易发生的日期越久，反之则表示客户交易发生的日期越近。\n",
    "- F（Frequency）：客户在最近一段时间内交易的次数。F值越大，表示客户交易越频繁，反之则表示客户交易不够活跃。\n",
    "- M（Monetary）：客户在最近一段时间内交易的金额。M值越大，表示客户价值越高，反之则表示客户价值越低。\n",
    " \n",
    "RFM分析就是根据客户活跃程度和交易金额的贡献，进行客户价值细分的一种方法。\n",
    "​\n",
    "首先构建RFM表。\n",
    "\n",
    "---\n",
    "\n",
    "**RFM** is a method used for analyzing customer value. It is commonly used in database marketing and direct marketing and has received particular attention in retail and professional services industries\n",
    "\n",
    "RFM stands for the three dimensions:\n",
    "\n",
    "1. Recency – How recently did the customer purchase?\n",
    "2. Frequency – How often do they purchase?\n",
    "3. Monetary Value – How much do they spend?\n",
    "\n",
    "\n",
    "Before starting RFM analysis we need to set objective and outcome of analysis, for this example goal is to define class of customer example - Platinum, Gold and Silver.\n",
    "1. Platinum customer - frequent and more revenue generator.\n",
    "2. Gold customer - frequent but less revenue generator.\n",
    "3. silver customer - less frequent and less revenue generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_id": 86
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Duplicate key in file PosixPath('/opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/matplotlibrc'), line 768 ('font.family  : sans-serif')\n",
      "Duplicate key in file PosixPath('/opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/matplotlibrc'), line 769 ('font.sans-serif : SimHei, DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif')\n",
      "Duplicate key in file PosixPath('/opt/conda/lib/python3.7/site-packages/matplotlib/mpl-data/matplotlibrc'), line 770 ('axes.unicode_minus : False')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cell_id": 4,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "\n",
    "# 将列名中的空格和横杠转换为下划线\n",
    "df.columns = df.columns.str.replace(' ', '_').str.replace('-', '_').str.lower()\n",
    "\n",
    "df = df.query('quantity>0 and unitprice>0')\n",
    "# 将日期字符串转换为 datetime 对象\n",
    "df['invoicedate'] = pd.to_datetime(df['invoicedate'].str.split(' ',expand=True).iloc[:,0])\n",
    "#df.drop_duplicates(subset=['customerid','invoiceno'], keep=\"first\", inplace=True) \n",
    "\n",
    "# Post Code 字段含有很多缺失值，删掉\n",
    "df.dropna(subset=['customerid'],how='all',inplace=True)\n",
    "\n",
    "# 增加用于分组的字段\n",
    "df['year'] = df.invoicedate.dt.year\n",
    "df['month'] = df.invoicedate.dt.month\n",
    "df['months'] = df.year.astype(str) + '-' + df.month.astype(str)\n",
    "df['totalcost'] =  df['unitprice'] * df['quantity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cell_id": 54
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoiceno</th>\n",
       "      <th>stockcode</th>\n",
       "      <th>description</th>\n",
       "      <th>quantity</th>\n",
       "      <th>invoicedate</th>\n",
       "      <th>unitprice</th>\n",
       "      <th>customerid</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>months</th>\n",
       "      <th>totalcost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2010</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoiceno stockcode                          description  quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "  invoicedate  unitprice  customerid         country  year  month   months  \\\n",
       "0  2010-12-01       2.55     17850.0  United Kingdom  2010     12  2010-12   \n",
       "1  2010-12-01       3.39     17850.0  United Kingdom  2010     12  2010-12   \n",
       "2  2010-12-01       2.75     17850.0  United Kingdom  2010     12  2010-12   \n",
       "3  2010-12-01       3.39     17850.0  United Kingdom  2010     12  2010-12   \n",
       "4  2010-12-01       3.39     17850.0  United Kingdom  2010     12  2010-12   \n",
       "\n",
       "   totalcost  \n",
       "0      15.30  \n",
       "1      20.34  \n",
       "2      22.00  \n",
       "3      20.34  \n",
       "4      20.34  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_id": 6
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoicedate</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>2011-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2011-12-07</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>2011-09-25</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>2011-11-21</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>2011-02-02</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>310.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           invoicedate  Frequency  Monetary  Recency\n",
       "customerid                                          \n",
       "12346.0     2011-01-18          1  77183.60    325.0\n",
       "12347.0     2011-12-07          7   4310.00      2.0\n",
       "12348.0     2011-09-25          4   1797.24     75.0\n",
       "12349.0     2011-11-21          1   1757.55     18.0\n",
       "12350.0     2011-02-02          1    334.40    310.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"quantity\"]] = df[[\"quantity\"]].astype(int)\n",
    "rfm = df.pivot_table(\n",
    "    index='customerid',\n",
    "    values = [\"invoiceno\",\"totalcost\",\"invoicedate\"],\n",
    "    aggfunc={\n",
    "        \"invoiceno\": pd.Series.nunique,\n",
    "        \"totalcost\" :\"sum\",\n",
    "        \"invoicedate\": \"max\"\n",
    "    }\n",
    ")\n",
    "\n",
    "# 所有用户最大的交易日期为标准，求每笔交易的时间间隔即为R\n",
    "rfm['Recency'] = (rfm.invoicedate.max() - rfm.invoicedate) / np.timedelta64(1, 'D')\n",
    "\n",
    "# # 每个客户的总销量即为F，总销售额即为M\n",
    "rfm.rename(columns={\n",
    "        \"invoiceno\": \"Frequency\",\n",
    "        \"totalcost\": \"Monetary\"\n",
    "    }, inplace = True)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": 7
   },
   "source": [
    "如最近在做用户调研，根据用户的打分，将用户进行归类，在实际操作中，将用户归为三类（0-6一类，7-8一类等）\n",
    "\n",
    "接着对客户价值进行标注，将客户分为8个等级。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cell_id": 8
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoicedate</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Recency</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.0</th>\n",
       "      <td>2011-01-18</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>325.0</td>\n",
       "      <td>重要挽留客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2011-12-07</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>2011-09-25</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>75.0</td>\n",
       "      <td>一般发展客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>2011-11-21</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>18.0</td>\n",
       "      <td>一般发展客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>2011-02-02</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>310.0</td>\n",
       "      <td>一般挽留客户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           invoicedate  Frequency  Monetary  Recency   label\n",
       "customerid                                                  \n",
       "12346.0     2011-01-18          1  77183.60    325.0  重要挽留客户\n",
       "12347.0     2011-12-07          7   4310.00      2.0  重要保持客户\n",
       "12348.0     2011-09-25          4   1797.24     75.0  一般发展客户\n",
       "12349.0     2011-11-21          1   1757.55     18.0  一般发展客户\n",
       "12350.0     2011-02-02          1    334.40    310.0  一般挽留客户"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 基于平均值做比较，超过均值为1，否则为0\n",
    "rfm[['Recency','Frequency','Monetary']].apply(lambda x:(x-x.mean())/x.std())\n",
    "\n",
    "def rfm_func(x):\n",
    "    level = x.apply(lambda x:'1'if x>0 else '0')\n",
    "    level = level.Recency + level.Frequency + level.Monetary\n",
    "    d = {\n",
    "        \"111\": \"重要价值客户\",\n",
    "        \"011\": \"重要保持客户\",\n",
    "        \"101\": \"重要挽留客户\",\n",
    "        \"001\": \"重要发展客户\",\n",
    "        \"110\": \"一般价值客户\",\n",
    "        \"010\": \"一般保持客户\",\n",
    "        \"100\": \"一般挽留客户\",\n",
    "        \"000\": \"一般发展客户\"\n",
    "    }\n",
    "    result = d[level]\n",
    "    return result\n",
    "\n",
    "rfm['label']= rfm[['Recency','Frequency','Monetary']].apply(lambda x:(x-x.mean())/x.std()).apply(rfm_func, axis=1)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": 9
   },
   "source": [
    "对重要价值客户和非重要价值客户进行可视化展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cell_id": 10
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Frequency', ylabel='Recency'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfm.loc[rfm.label == \"重要价值客户\", \"color\"] = \"g\"\n",
    "rfm.loc[~(rfm.label == \"重要价值客户\"),'color'] = \"r\"\n",
    "rfm.plot.scatter(\"Frequency\", \"Recency\", c=rfm.color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": 64
   },
   "source": [
    "但是股票交易场景下，对于客户的分层和定义会不一样。\n",
    "\n",
    "我们常使用kmeans做聚类，再进一步分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": 52
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get right number of cluster for K-means so we neeed to loop from 1 to 20 number of cluster and check score.\n",
    "#Elbow method is used to represnt that. \n",
    "Nc = range(1, 20)\n",
    "kmeans = [KMeans(n_clusters=i) for i in Nc]\n",
    "\n",
    "rfm_segmentation = rfm[['Recency','Frequency','Monetary']].copy()\n",
    "score = [kmeans[i].fit(rfm_segmentation).score(rfm_segmentation) for i in range(len(kmeans))]\n",
    "plt.plot(Nc,score)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Elbow Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": 11
   },
   "source": [
    "通过RFM识别不同的客户群体，能够衡量客户价值和客户利润创收能力，可以指定个性化的沟通和营销服务，为更多的营销决策提供有力支持，为企业创造更大的利益。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_id": 19
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Recency</th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12347.0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.0</th>\n",
       "      <td>75.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.0</th>\n",
       "      <td>310.0</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.0</th>\n",
       "      <td>36.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2506.04</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12353.0</th>\n",
       "      <td>204.0</td>\n",
       "      <td>1</td>\n",
       "      <td>89.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12354.0</th>\n",
       "      <td>232.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1079.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12355.0</th>\n",
       "      <td>214.0</td>\n",
       "      <td>1</td>\n",
       "      <td>459.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12356.0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2811.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12357.0</th>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6207.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetary  cluster\n",
       "customerid                                       \n",
       "12347.0         2.0          7   4310.00        0\n",
       "12348.0        75.0          4   1797.24        0\n",
       "12349.0        18.0          1   1757.55        0\n",
       "12350.0       310.0          1    334.40        0\n",
       "12352.0        36.0          8   2506.04        0\n",
       "12353.0       204.0          1     89.00        0\n",
       "12354.0       232.0          1   1079.40        0\n",
       "12355.0       214.0          1    459.40        0\n",
       "12356.0        22.0          3   2811.43        0\n",
       "12357.0        33.0          1   6207.67        0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting data in Kmeans theorem.\n",
    "kmeans = KMeans(n_clusters=3, random_state=0).fit(rfm_segmentation)\n",
    "\n",
    "# this creates a new column called cluster which has cluster number for each row respectively.\n",
    "rfm_segmentation['cluster'] = kmeans.labels_\n",
    "\n",
    "#check our hypothesis\n",
    "rfm_segmentation[rfm_segmentation.cluster == 0].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cell_id": 55
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cluster', ylabel='Recency'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "cluster 0 have high recency rate which is bad. cluster 1 and cluster 2 having low so they are in race of platinum\n",
    "and gold customer.\n",
    "'''\n",
    "sns.boxplot(x=rfm_segmentation.cluster,y=rfm_segmentation.Recency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "cell_id": 56
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cluster', ylabel='Frequency'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=rfm_segmentation.cluster,y=rfm_segmentation.Frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cell_id": 57
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cluster', ylabel='Monetary'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=rfm_segmentation.cluster,y=rfm_segmentation.Monetary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": 58
   },
   "source": [
    "Based on customer Segmentation we found out cluster 2 is Platinum customers Cluster 1 is Gold Customers Cluster 3 is Silver Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cell_id": 66
   },
   "outputs": [],
   "source": [
    "# Arguments (x = value, p = recency, monetary_value, frequency, d = quartiles dict)\n",
    "\n",
    "#lower the recency, good for store..\n",
    "def RScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "    \n",
    "# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)\n",
    "\n",
    "#higher value of frequency and monetary lead to a good consumer. here higher value = 1 in reverse way.\n",
    "\n",
    "def FMScore(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cell_id": 67
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Monetary</th>\n",
       "      <th>Recency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>1.0</td>\n",
       "      <td>307.415</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>2.0</td>\n",
       "      <td>674.485</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1661.740</td>\n",
       "      <td>141.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Frequency  Monetary  Recency\n",
       "0.25        1.0   307.415    17.00\n",
       "0.50        2.0   674.485    50.00\n",
       "0.75        5.0  1661.740   141.75"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "quantile is like 25% , 50% and 75% level of values. example if we have 100 values first 25 in 1st quartile 25% \n",
    "then second contain next 25% which is 50% and there after next 25% which is 75% and 4th quartile is more than 75% \n",
    "values.\n",
    "'''\n",
    "quantile = rfm.quantile(q=[0.25,0.5,0.75])\n",
    "quantile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "cell_id": 68
   },
   "outputs": [],
   "source": [
    "rfm_segmentation['R_Quartile'] = rfm_segmentation['Recency'].apply(RScore,args=('Recency',quantile))\n",
    "rfm_segmentation['F_Quartile'] = rfm_segmentation['Frequency'].apply(FMScore, args=('Frequency',quantile))\n",
    "rfm_segmentation['M_Quartile'] = rfm_segmentation['Monetary'].apply(FMScore, args=('Monetary',quantile))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "cell_id": 69
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Frequency': {0.25: 1.0, 0.5: 2.0, 0.75: 5.0},\n",
       " 'Monetary': {0.25: 307.41499999999996,\n",
       "  0.5: 674.4849999999999,\n",
       "  0.75: 1661.7400000000007},\n",
       " 'Recency': {0.25: 17.0, 0.5: 50.0, 0.75: 141.75}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantile.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "cell_id": 70
   },
   "outputs": [
    {
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       "      <td>2</td>\n",
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       "      <td>241</td>\n",
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       "      <td>443</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency  Monetary  cluster  R_Quartile  F_Quartile  \\\n",
       "customerid                                                                  \n",
       "12346.0       325.0          1  77183.60        1           4           4   \n",
       "12347.0         2.0          7   4310.00        0           1           1   \n",
       "12348.0        75.0          4   1797.24        0           3           2   \n",
       "12349.0        18.0          1   1757.55        0           2           4   \n",
       "12350.0       310.0          1    334.40        0           4           4   \n",
       "\n",
       "            M_Quartile RFMScore  \n",
       "customerid                       \n",
       "12346.0              1      441  \n",
       "12347.0              1      111  \n",
       "12348.0              1      321  \n",
       "12349.0              1      241  \n",
       "12350.0              3      443  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate RFM score..\n",
    "\n",
    "rfm_segmentation['RFMScore'] = rfm_segmentation.R_Quartile.map(str) \\\n",
    "                            + rfm_segmentation.F_Quartile.map(str) \\\n",
    "                            + rfm_segmentation.M_Quartile.map(str)\n",
    "rfm_segmentation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "cell_id": 71
   },
   "outputs": [],
   "source": [
    "'''\n",
    "Another possibility is to combine the scores to create one score (eg. 4+1+1). This will create a score \n",
    "between 3 and 12. Here the advantage is that each of the scores got same importance. However some scores \n",
    "will have many segments as constituents (eg - 413 ad 431)\n",
    "'''\n",
    "\n",
    "rfm_segmentation['Total_score'] = rfm_segmentation['R_Quartile'] + rfm_segmentation['F_Quartile'] + \\\n",
    "rfm_segmentation['M_Quartile']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "cell_id": 72
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "            Recency  Frequency  Monetary  cluster  R_Quartile  F_Quartile  \\\n",
       "customerid                                                                  \n",
       "12346.0       325.0          1  77183.60        1           4           4   \n",
       "12347.0         2.0          7   4310.00        0           1           1   \n",
       "12348.0        75.0          4   1797.24        0           3           2   \n",
       "12349.0        18.0          1   1757.55        0           2           4   \n",
       "12350.0       310.0          1    334.40        0           4           4   \n",
       "\n",
       "            M_Quartile RFMScore  Total_score  \n",
       "customerid                                    \n",
       "12346.0              1      441            9  \n",
       "12347.0              1      111            3  \n",
       "12348.0              1      321            6  \n",
       "12349.0              1      241            7  \n",
       "12350.0              3      443           11  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "cell_id": 73
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Customers:  456\n",
      "Loyal Customers:  872\n",
      "Big Spenders:  1085\n",
      "Almost Lost:  34\n",
      "Lost Customers:  208\n",
      "Lost Cheap Customers:  444\n"
     ]
    }
   ],
   "source": [
    "print(\"Best Customers: \",len(rfm_segmentation[rfm_segmentation['RFMScore']=='111']))\n",
    "print('Loyal Customers: ',len(rfm_segmentation[rfm_segmentation['F_Quartile']==1]))\n",
    "print(\"Big Spenders: \",len(rfm_segmentation[rfm_segmentation['M_Quartile']==1]))\n",
    "print('Almost Lost: ', len(rfm_segmentation[rfm_segmentation['RFMScore']=='134']))\n",
    "print('Lost Customers: ',len(rfm_segmentation[rfm_segmentation['RFMScore']=='344']))\n",
    "print('Lost Cheap Customers: ',len(rfm_segmentation[rfm_segmentation['RFMScore']=='444']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "cell_id": 75
   },
   "outputs": [
    {
     "data": {
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       "      <th>14911.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>201</td>\n",
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       "      <td>3</td>\n",
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       "      <td>9.0</td>\n",
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       "      <th>17102.0</th>\n",
       "      <td>261.0</td>\n",
       "      <td>1</td>\n",
       "      <td>25.50</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15823.0</th>\n",
       "      <td>372.0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0</td>\n",
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       "      <td>444</td>\n",
       "      <td>12</td>\n",
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       "      <th>17763.0</th>\n",
       "      <td>263.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>17956.0</th>\n",
       "      <td>249.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.75</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>12</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4338 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Recency  Frequency   Monetary  cluster  R_Quartile  F_Quartile  \\\n",
       "customerid                                                                   \n",
       "14646.0         1.0         73  280206.02        2           1           1   \n",
       "18102.0         0.0         60  259657.30        2           1           1   \n",
       "17450.0         8.0         46  194550.79        2           1           1   \n",
       "14911.0         1.0        201  143825.06        2           1           1   \n",
       "14156.0         9.0         55  117379.63        1           1           1   \n",
       "...             ...        ...        ...      ...         ...         ...   \n",
       "17102.0       261.0          1      25.50        0           4           4   \n",
       "15823.0       372.0          1      15.00        0           4           4   \n",
       "17763.0       263.0          1      15.00        0           4           4   \n",
       "17956.0       249.0          1      12.75        0           4           4   \n",
       "16738.0       297.0          1       3.75        0           4           4   \n",
       "\n",
       "            M_Quartile RFMScore  Total_score  \n",
       "customerid                                    \n",
       "14646.0              1      111            3  \n",
       "18102.0              1      111            3  \n",
       "17450.0              1      111            3  \n",
       "14911.0              1      111            3  \n",
       "14156.0              1      111            3  \n",
       "...                ...      ...          ...  \n",
       "17102.0              4      444           12  \n",
       "15823.0              4      444           12  \n",
       "17763.0              4      444           12  \n",
       "17956.0              4      444           12  \n",
       "16738.0              4      444           12  \n",
       "\n",
       "[4338 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.sort_values(by=['RFMScore', 'Monetary'], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cell_id": 76
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFMScore\n",
       "111    9547.540000\n",
       "112    1286.331270\n",
       "113     511.270000\n",
       "114     253.920000\n",
       "121    2910.558864\n",
       "          ...     \n",
       "434     214.629524\n",
       "441    8910.267273\n",
       "442     994.800000\n",
       "443     415.126847\n",
       "444     165.206036\n",
       "Name: Monetary, Length: 61, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.groupby('RFMScore')['Monetary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "cell_id": 77
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total_score\n",
       "3     9547.540000\n",
       "4     4164.127041\n",
       "5     2431.437589\n",
       "6     1456.718650\n",
       "7     1201.251631\n",
       "8      782.687576\n",
       "9      717.969937\n",
       "10     384.770298\n",
       "11     288.072454\n",
       "12     165.206036\n",
       "Name: Monetary, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm_segmentation.groupby('Total_score')['Monetary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "cell_id": 78
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_score'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Based on Monetary we see Total_score 3,4,5 have highest value which is good for model. because it could have\n",
    "combination of values such as 111, 121, 122 etc.\n",
    "'''\n",
    "rfm_segmentation.groupby('Total_score')['Monetary'].mean().plot(kind='bar', colormap='Blues_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "cell_id": 79
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_score'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Based on Frequncy we see Total_score 3,4,5 have highest value which is good for model. because it could have\n",
    "combination of values such as 111, 121, 122 etc.\n",
    "'''\n",
    "rfm_segmentation.groupby('Total_score')['Frequency'].mean().plot(kind='bar', colormap='Blues_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "cell_id": 85
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total_score'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Based on Recency we see Total_score 10,11,12 have highest value which is good for model. because it could have\n",
    "combination of values such as 444, 434, 334 etc.\n",
    "'''\n",
    "\n",
    "rfm_segmentation.groupby('Total_score')['Recency'].mean().plot(kind='bar', colormap='Blues_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "cell_id": 82
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = rfm_segmentation.groupby('Total_score')['Monetary'].mean()\n",
    "f = rfm_segmentation.groupby('Total_score')['Frequency'].mean()\n",
    "r = rfm_segmentation.groupby('Total_score')['Recency'].mean()\n",
    "\n",
    "plt.style.use('bmh')\n",
    "fig, ax1 = plt.subplots(figsize = [10,5])\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.plot(m.index, m.values,color=color)\n",
    "ax1.set_title('RFM Analysis')\n",
    "ax1.set_xlabel('Total Score')\n",
    "ax1.set_ylabel('Monetary', color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.plot(f.index, f.values,color=color)\n",
    "ax2.set_ylabel('Frequency', color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax3 = ax2.twinx()\n",
    "\n",
    "color = 'tab:green'\n",
    "ax3.plot(r.index, r.values,color=color)\n",
    "ax3.set_ylabel('Recency', color=color)\n",
    "ax3.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "#fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.show()"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
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  "max_cell_id": 88,
  "serving": {
   "http": {
    "cache": {
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    "mode": "immutable"
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   "timer": {
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    "expression": "00 10 * * *"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
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 "nbformat": 4,
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